SJTU Pujiang International College PhD Student Publishes First-Author Paper on Robot Target Search in IEEE T-RO
Overview
Yunpeng Lu, a 2024 doctoral student at Shanghai Jiao Tong University's Pujiang International College, has been accepted as first author of a paper titled 'RPT*: Global Planning with Probabilistic Terminals for Target Search in Complex Environments' in IEEE Transactions on Robotics. The work, co-authored with Carnegie Mellon University and developed at the RAP Lab, introduces RPT*, an algorithm with optimality guarantees for target search under uncertainty, and the hierarchical autonomous target search system HATS. Validated in simulation and on real robots (quadruped and wheeled), the system addresses probabilistic termination in Hamiltonian path problems, achieving significant speedups over integer programming and outperforming greedy and reinforcement learning baselines in expected path cost.
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Control: reported autonomous
“研究面向复杂环境下移动机器人目标搜索问题，提出了一类具备最优性保证的全局规划算法RPT*，构建出层次化自主目标搜索系统HATS，目前已在仿真环境与真实机器人平台上完成完整验证。”
Exact source · revision 2Source owner not reported
Reported quantity · Reported success rate: 100 percent · reported trials
“在未知环境搜索（HATS-U）实验中，RPT*在准确先验和误导性先验两种场景下均实现了100%成功率。”
Exact source · revision 2Source owner not reported
Reported quantity · elapsed time: 152 other · Basis not reported · Differing source assertions
“左：四足机器人在40×30米走廊环境中搜索目标（书包），任务时长152秒。”
Exact source · revision 2Source owner not reported
Reported quantity · elapsed time: 104 other · Basis not reported · Differing source assertions
“右：轮式机器人在30×20米实验室环境中搜索目标（垃圾桶），任务时长104秒。”
Exact source · revision 2Source owner not reported
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- Chinese robotics — Research news discoverySignalSJTU Pujiang International College PhD Student Publishes First-Author Paper on Robot Target Search in IEEE T-RO
Yunpeng Lu, a 2024 doctoral student at Shanghai Jiao Tong University's Pujiang International College, has been accepted as first author of a paper titled 'RPT*: Global Planning with Probabilistic Terminals for Target Search in Complex Environments' in IEEE Transactions on Robotics. The work, co-authored with Carnegie Mellon University and developed at the RAP Lab, introduces RPT*, an algorithm with optimality guarantees for target search under uncertainty, and the hierarchical autonomous target search system HATS. Validated in simulation and on real robots (quadruped and wheeled), the system addresses probabilistic termination in Hamiltonian path problems, achieving significant speedups over integer programming and outperforming greedy and reinforcement learning baselines in expected path cost.
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